Impact Factor:7.196

Journal:Pattern Recognition

Abstract:Image manipulation detection has attracted considerable attention owing to the increasing security risks posed by fake images. Previous studies have proven that tampering traces hidden in images are essen- tial for detecting manipulated regions. However, existing methods have limitations in generalization and the ability to tackle post-processing methods. This paper presents a novel Network to learn and Enhance Multiple tampering Traces (EMT-Net), including noise distribution and visual artifacts. For better gener- alization, EMT-Net extracts global and local noise features from noise maps using transformers and cap- tures local visual artifacts from original RGB images using convolutional neural networks. Moreover, we enhance fused tampering traces using the proposed edge artifacts enhancement modules and edge su- pervision strategy to discover subtle edge artifacts hidden in images. Thus, EMT-Net can prevent the risks of losing slight visual clues against well-designed post-processing methods. Experimental results indicate that the proposed method can detect manipulated regions and outperform state-of-the-art approaches under comprehensive quantitative metrics and visual qualities. In addition, EMT-Net shows robustness when various post-processing methods further manipulate images.

Translation or Not:no

Date of Publication:2022-09-01

Included Journals:SCI

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